Unsere Stipendiaten

Stipendiaten 2026

Gruppenfoto mit (von links nach rechts) Ndim Handson Nsani, Arlette Musanabera , Vongai Mitchell Makuwaza, Dzodzoenyenye Senanou und Benedict Ositadinma Ejelonu — Von links nach rechts: Ndim Handson Nsani, Arlette Musanabera , Vongai Mitchell Makuwaza, Dzodzoenyenye Senanou und Benedict Ositadinma Ejelonu.

Benedict Ositadinma Ejelonu

Nigeria

Supervisor: Reinhard Drews

Forschungsprojekt:
tbd

Vongai Mitchell Makuwaza

Simbabwe

Supervisor: Kristof Meding

Forschungsprojekt:
When EU Courts ask for explanations: What should you do?

Ndim Handson Nsani

Kamerun

Supervisor: Steffen Borrmann

Forschungsprojekt:
Mapping the burden of Plasmodium Malariae: A spatial and temporal modeling study

Dzodzoenyenye Senanou

Togo

Supervisor: Oliver Kohlbacher

Forschungsprojekt:
Machine Learning-Based Detection of Inconsistent DIA Experiments Using DIAuditor Quality Metrics

Arlette Musanabera

Ruanda

Supervisor: Matthias Ritter

Forschungsprojekt:
Improving Short-term Forecasting of Global Daily Exchange Rates Using Lightweight Deep-learning Models

Stipendiaten 2025

Die AIMS-Fellows posieren neben dem Schild des Tübingen AI Research Building. Von links nach rechts: Claire Olivic Dongmo, Ignatius Boadi, Alvine Fandio, Détagnon Bernic Gbaguidi und Duaa Alshareif. — Die fünf AIMS-Stipendiaten in Tübingen. Von links nach rechts: Claire Olivic Dongmo, Ignatius Boadi, Alvine Fandio, Détagnon Bernic Gbaguidi and Duaa Alshareif.

Kamerun

Supervisor: Martin Butz

Forschungsprojekt:
tbd

Kamerun

Supervisor: Nicole Ludwig

Forschungsprojekt:
Impact of extreme weather events on solar energy production

Benin

Supervisor: Nico Pfeifer

Forschungsprojekt:
TabPFN to fight infectious diseases

Ghana

Supervisor: Kerstin Ritter

Forschungsprojekt:
Longitudinal brain imaging in at risk relatives of SPG4 Patients

Sudan

Supervisor: Michael Franke

Forschungsprojekt:
AnaphoraGym: a Benchmark for Anaphora Resolution in Language Models

Stipendiaten 2024

Fifamé Edwige Akpoly

Fifamé Edwige Akpoly
Benin

Supervisor: Kira Rehfeld

Forschungsprojekt:
Rainfall Forecasting in the Sahel Region Using Machine Learning Techniques

Stephen Kiilu

Stephen Kiilu
Kenya

Supervisor: Carsten Eickhoff

Forschungsprojekt:
Integration of Knowledge in LLMs

Immaculate Wanjiru Kimani

Immaculate Wanjiru Kimani
Kenya

Supervisor: Dominik Papies

Forschungsprojekt:
Exploring Financial Inclusion through Social Media Influencers in Emerging Economies

Pauline Ornela Megne Choudja

Pauline Ornela Megne Choudja
Kamerun

Supervisor: Thomas Küstner

Forschungsprojekt:
Longitudinal Tracking and Segmentation of Lesions in PET/CT Hybrid Imaging

Berthine Nyunga Mpinda

Berthine Nyunga Mpinda
Demokratische Republik Kongo

Supervisor: Hendrik Lensch

Forschungsprojekt:
Explainable Medical Image Classification

Stipendiaten 2023

Sudan

Supervisor: Christian Baumgartner

Demokratische Republik Kongo

Supervisor: Ulrike von Luxburg

Machine learning and statistics for climate networks
In this project, we investigate novel methods to assess the uncertainty in climate network construction and estimation procedures. The goal will be to create uncertainty estimates, not by network subsampling, but by bootstrapping the underlying time series directly. Different approaches will be compared, both on a theoretical and implementation level and connected to background knowledge in climate science.

Ifeoma Veronica Nwabufo

Nigeria

Supervisor: Philipp Berens

Unsupervised learning in medical data science
The project will look at unsupervised learning methods recently developed in the lab to embed medical images via contrastive learning. It will explore the resulting embedding, study which data transformations are important and whether it can be related to disease/demographic factors.

Mendrika Rakotomanga

Madagaskar

Supervisor: Bernhard Schölkopf

Bayesian inference of gravitational waves with machine learning
This project is concerned with the evaluation and development of machine learning methods for gravitational wave inference. One focus is the analysis of current methods with regard to possible biases and their impact on large-scale analyses. In this context, the project also aims to develop methods to mitigate possible inaccuracies.

Fenosoa Randrianjatovo

Madagaskar

Supervisor: Claire Vernade

About random strategies in (non-stationary) reinforcement learning
Reinforcement learning (RL) algorithms have so far been developed mainly for steady-state environments and have difficulty adapting when the system dynamics or the reward function changes. Posterior sampling and Thompson sampling were identified early on as efficient strategies in RL, in part due to their randomised strategies. While some algorithms such as UCRL have recently been adapted to non-stationary environments, no randomised strategy has been proposed yet. In this project, we aim to propose a randomised -and more practical- algorithm that builds on posterior sampling and is capable of achieving sublinear regret. We will start by studying a (possibly context-dependent) bandit problem and then extend our findings to more complex RL models.

Stipendiaten 2022

Bolaji Bamiro

| Wir trauern um Bolaji, die am 19. September 2023 auf tragische Weise bei einem Autounfall ums Leben kam. Unsere Gedanken sind bei der Familie und den Freunden von Bolaji. Wir erinnern uns mit Freude an unsere Zeit mit ihr in Tübingen. |

Nigeria

Simbabwe

Kamerun

Togo

Ruanda

Kamerun

Kamerun

Benin

Ghana

Sudan

Fifamé Edwige AkpolyBenin

Stephen KiiluKenya

Immaculate Wanjiru KimaniKenya

Pauline Ornela Megne ChoudjaKamerun

Berthine Nyunga MpindaDemokratische Republik Kongo

Amel Abdulraheem

Sudan

Albert Agisha

Demokratische Republik Kongo

Ifeoma Veronica Nwabufo

Nigeria

Mendrika Rakotomanga

Madagaskar

Fenosoa Randrianjatovo

Madagaskar

Fifamé Edwige Akpoly
Benin

Stephen Kiilu
Kenya

Immaculate Wanjiru Kimani
Kenya

Pauline Ornela Megne Choudja
Kamerun

Berthine Nyunga Mpinda
Demokratische Republik Kongo